// Parallel Definitions
void ParallelFor(std::function<void(int64_t)> func, int64_t count,
int chunkSize) {
CHECK(threads.size() > 0 || MaxThreadIndex() == 1);
// Run iterations immediately if not using threads or if _count_ is small
if (threads.empty() || count < chunkSize) {
for (int64_t i = 0; i < count; ++i) func(i);
return;
}
// Create and enqueue _ParallelForLoop_ for this loop
ParallelForLoop loop(std::move(func), count, chunkSize,
CurrentProfilerState());
workListMutex.lock();
loop.next = workList;
workList = &loop;
workListMutex.unlock();
// Notify worker threads of work to be done
std::unique_lock<std::mutex> lock(workListMutex);
workListCondition.notify_all();
// Help out with parallel loop iterations in the current thread
while (!loop.Finished()) {
// Run a chunk of loop iterations for _loop_
// Find the set of loop iterations to run next
int64_t indexStart = loop.nextIndex;
int64_t indexEnd = std::min(indexStart + loop.chunkSize, loop.maxIndex);
// Update _loop_ to reflect iterations this thread will run
loop.nextIndex = indexEnd;
if (loop.nextIndex == loop.maxIndex) workList = loop.next;
loop.activeWorkers++;
// Run loop indices in _[indexStart, indexEnd)_
lock.unlock();
for (int64_t index = indexStart; index < indexEnd; ++index) {
uint64_t oldState = ProfilerState;
ProfilerState = loop.profilerState;
if (loop.func1D) {
loop.func1D(index);
}
// Handle other types of loops
else {
CHECK(loop.func2D);
loop.func2D(Point2i(index % loop.nX, index / loop.nX));
}
ProfilerState = oldState;
}
lock.lock();
// Update _loop_ to reflect completion of iterations
loop.activeWorkers--;
}
}
void ParallelFor2D(std::function<void(Point2i)> func, const Point2i &count) {
CHECK(threads.size() > 0 || MaxThreadIndex() == 1);
if (threads.empty()) {
for (int y = 0; y < count.y; ++y)
for (int x = 0; x < count.x; ++x) func(Point2i(x, y));
return;
}
ParallelForLoop loop(std::move(func), count, CurrentProfilerState());
{
std::lock_guard<std::mutex> lock(workListMutex);
loop.next = workList;
workList = &loop;
}
std::unique_lock<std::mutex> lock(workListMutex);
workListCondition.notify_all();
// Help out with parallel loop iterations in the current thread
while (!loop.Finished()) {
// Run a chunk of loop iterations for _loop_
// Find the set of loop iterations to run next
int64_t indexStart = loop.nextIndex;
int64_t indexEnd = std::min(indexStart + loop.chunkSize, loop.maxIndex);
// Update _loop_ to reflect iterations this thread will run
loop.nextIndex = indexEnd;
if (loop.nextIndex == loop.maxIndex) workList = loop.next;
loop.activeWorkers++;
// Run loop indices in _[indexStart, indexEnd)_
lock.unlock();
for (int64_t index = indexStart; index < indexEnd; ++index) {
uint64_t oldState = ProfilerState;
ProfilerState = loop.profilerState;
if (loop.func1D) {
loop.func1D(index);
}
// Handle other types of loops
else {
CHECK(loop.func2D);
loop.func2D(Point2i(index % loop.nX, index / loop.nX));
}
ProfilerState = oldState;
}
lock.lock();
// Update _loop_ to reflect completion of iterations
loop.activeWorkers--;
}
}
void ParallelInit() {
CHECK_EQ(threads.size(), 0);
int nThreads = MaxThreadIndex();
ThreadIndex = 0;
// Create a barrier so that we can be sure all worker threads get past
// their call to ProfilerWorkerThreadInit() before we return from this
// function. In turn, we can be sure that the profiling system isn't
// started until after all worker threads have done that.
std::shared_ptr<Barrier> barrier = std::make_shared<Barrier>(nThreads);
// Launch one fewer worker thread than the total number we want doing
// work, since the main thread helps out, too.
for (int i = 0; i < nThreads - 1; ++i)
threads.push_back(std::thread(workerThreadFunc, i + 1, barrier));
barrier->Wait();
}
SpatialLightDistribution::SpatialLightDistribution(const Scene &scene,
int maxVoxels)
: scene(scene) {
// Compute the number of voxels so that the widest scene bounding box
// dimension has maxVoxels voxels and the other dimensions have a number
// of voxels so that voxels are roughly cube shaped.
Bounds3f b = scene.WorldBound();
Vector3f diag = b.Diagonal();
Float bmax = diag[b.MaximumExtent()];
for (int i = 0; i < 3; ++i)
nVoxels[i] = std::max(1, int(std::round(diag[i] / bmax * maxVoxels)));
LOG(INFO) << "SpatialLightDistribution: scene bounds " << b <<
", voxel res (" << nVoxels[0] << ", " << nVoxels[1] << ", " <<
nVoxels[2] << ")";
// It's important to pre-size the localVoxelDistributions vector, to
// avoid race conditions with one thread resizing the vector while
// another is reading from it.
localVoxelDistributions.resize(MaxThreadIndex());
}
void MLTIntegrator::Render(const Scene &scene) {
ProfilePhase p(Prof::IntegratorRender);
std::unique_ptr<Distribution1D> lightDistr =
ComputeLightPowerDistribution(scene);
// Generate bootstrap samples and compute normalization constant $b$
int nBootstrapSamples = nBootstrap * (maxDepth + 1);
std::vector<Float> bootstrapWeights(nBootstrapSamples, 0);
if (scene.lights.size() > 0) {
ProgressReporter progress(nBootstrap / 256,
"Generating bootstrap paths");
std::vector<MemoryArena> bootstrapThreadArenas(MaxThreadIndex());
int chunkSize = Clamp(nBootstrap / 128, 1, 8192);
ParallelFor([&](int i) {
// Generate _i_th bootstrap sample
MemoryArena &arena = bootstrapThreadArenas[threadIndex];
for (int depth = 0; depth <= maxDepth; ++depth) {
int rngIndex = i * (maxDepth + 1) + depth;
MLTSampler sampler(mutationsPerPixel, rngIndex, sigma,
largeStepProbability, nSampleStreams);
Point2f pRaster;
bootstrapWeights[rngIndex] =
L(scene, arena, lightDistr, sampler, depth, &pRaster).y();
arena.Reset();
}
if ((i + 1 % 256) == 0) progress.Update();
}, nBootstrap, chunkSize);
progress.Done();
}
Distribution1D bootstrap(&bootstrapWeights[0], nBootstrapSamples);
Float b = bootstrap.funcInt * (maxDepth + 1);
// Run _nChains_ Markov chains in parallel
Film &film = *camera->film;
int64_t nTotalMutations =
(int64_t)mutationsPerPixel * (int64_t)film.GetSampleBounds().Area();
if (scene.lights.size() > 0) {
StatTimer timer(&renderingTime);
const int progressFrequency = 32768;
ProgressReporter progress(nTotalMutations / progressFrequency,
"Rendering");
ParallelFor([&](int i) {
int64_t nChainMutations =
std::min((i + 1) * nTotalMutations / nChains, nTotalMutations) -
i * nTotalMutations / nChains;
// Follow {i}th Markov chain for _nChainMutations_
MemoryArena arena;
// Select initial state from the set of bootstrap samples
RNG rng(i);
int bootstrapIndex = bootstrap.SampleDiscrete(rng.UniformFloat());
int depth = bootstrapIndex % (maxDepth + 1);
// Initialize local variables for selected state
MLTSampler sampler(mutationsPerPixel, bootstrapIndex, sigma,
largeStepProbability, nSampleStreams);
Point2f pCurrent;
Spectrum LCurrent =
L(scene, arena, lightDistr, sampler, depth, &pCurrent);
// Run the Markov chain for _nChainMutations_ steps
for (int64_t j = 0; j < nChainMutations; ++j) {
sampler.StartIteration();
Point2f pProposed;
Spectrum LProposed =
L(scene, arena, lightDistr, sampler, depth, &pProposed);
// Compute acceptance probability for proposed sample
Float accept = std::min((Float)1, LProposed.y() / LCurrent.y());
// Splat both current and proposed samples to _film_
if (accept > 0)
film.AddSplat(pProposed,
LProposed * accept / LProposed.y());
film.AddSplat(pCurrent, LCurrent * (1 - accept) / LCurrent.y());
// Accept or reject the proposal
if (rng.UniformFloat() < accept) {
pCurrent = pProposed;
LCurrent = LProposed;
sampler.Accept();
++acceptedMutations;
} else
sampler.Reject();
++totalMutations;
if ((i * nTotalMutations / nChains + j) % progressFrequency ==
0)
progress.Update();
arena.Reset();
}
}, nChains);
progress.Done();
}
// Store final image computed with MLT
camera->film->WriteImage(b / mutationsPerPixel);
}
